Trainings - Monday, May 16

Morning Training Sessions
7-9 a.m.

Trainings are add-ons and not included in the Symposium Pass registration. Each training session is $25 for USGIF Members and $30 for USGIF Non-Members. Sign up for training during the registration process.

This course will provide an in-depth exploration of the challenges exploiting low quality, unreliable OSINT and fusing OSINT with traditional sources. Attendees will learn about the fusion of uncertain data and the impact on performing activity-based intelligence over conflicting observations. Grounding the discussions will be motivating examples from three OSINT, multimodal data sets collected from social media and online resources.

Using the Analytic Decision Game to Teach Critical Thinking – Pennsylvania State University
Osceola Breakout Room 2

Are human analysts capable of making instantaneous assessments or are they simply restating what the sensor/computing “black-box” is producing? This presentation explores the use of the analytic decision game (ADG) as pedagogy to train the human analyst of the future – the digital native. The ADG is an adaptation of the military war game or simulation exercise.

In this course, attendees will learn basic foundational Agile methods, explaining how Agile is an expression of lean thinking. Attendees will learn the Agile roles, ceremonies, artifacts, and change management approaches and how they facilitate organizational adoption of lean principles and practices. The course will compare and contrast Agile to lean, Lean Six Sigma, traditional project management, and repeatable checklist operations.

This workshop demonstrates the use of unsupervised learning techniques to better understand human geography variables in new environments. More specifically, the course demonstrates the use of hierarchical clustering and principal components analysis to analyze a large set of socioeconomic and human geography attributes with no prior knowledge of the data.

Through case studies, attendees will learn to build better models that don’t over fit data. Featured predictive analytic methods will include several types of regression, neural networks, and decision trees. Most importantly, attendees will learn to randomly split your data into training, validation (tuning), and test subsets to prevent over fitting data, thus making predictions more robust.

This course will provide information about geospatial data and products produced by U.S. government remote sensing programs that can be obtained for use in research, outreach, and education from the U.S. Geological Survey.